The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication
Blake Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro · arXiv (Cornell University) · 2021
We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where $M$ machines work in parallel over the course of $R$ rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute $K$ stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.